New findings show invariance alone isn't enough to identify latent causal variables.
problem Lack of theoretical insights for identifying latent causal variables when variables are latent.
method Assessed the connection between invariance and causal representation learning using impossibility results.
result Invariance alone is insufficient to identify latent causal variables.
New method improves domain generalization by aligning causal mechanisms across domains.
problem Improving model's ability to generalize across different distributions.
method Introduces invariance of average causal effect of features to labels, regularizing training approach.
result Demonstrates superior performance on benchmark datasets compared to state-of-the-art methods.
Invariant Causal Set Covering Machines avoid spurious associations.
problem Learning algorithms for rule-based models are vulnerable to spurious associations.
method Building on invariant causal prediction, propose Invariant Causal Set Covering Machines for conjunctions/disjunctions of binary-valued rules.
result The method can identify causal parents of a variable of interest in polynomial time.
Study finds a method to discover causal relationships that are invariant to marginal distributions.
problem Current causal discovery methods are sensitive to marginal distributions, leading to unreliable results.
method Proposes a non-parametric estimator that marginalizes the marginals to find intrinsic causal relationships.
result The proposed method yields causal estimators competitive with current methodologies and emphasizes uncertainty.
FAIR-NN finds invariant variables for causal inference across diverse environments.
problem Nonparametric invariance and causal learning in regression models with varying joint distributions.
method FAIR-NN framework using adversarial optimization and neural networks.
result FAIR-NN identifies invariant variables and quasi-causal variables under minimal conditions.
This work tackles OOD generalization by leveraging causal invariance without needing to recover causal features.
problem Learning models that perform well on out-of-distribution (OOD) data.
method Causal invariant transformations to modify non-causal features while preserving causal parts.
result Theoretical and practical methods to learn a minimax optimal model across domains using single domain data.
ISL improves causal structure learning with invariant structures across different environments.
problem Improving causal structure discovery for better generalization and explainability.
method ISL splits data into environments, learns invariant structures, and selects optimal classifiers based on graph structures.
result ISL accurately discovers causal structures and outperforms alternative methods on synthetic and real-world datasets.
Unified approach to causal representation learning using invariance principles.
problem Identifying latent causal variables from high-dimensional observations.
method Guiding identification of causal variables with invariance principles rather than causal hierarchies.
result Unified method that mixes causal and non-causal assumptions improves treatment effect estimation.
Two environments are enough to infer causal graphs and counterfactuals.
problem Inferring causal relations from multiple environments, especially for nonlinear mechanisms.
method Using structural causal models and the invariance principle, the study shows that only two auxiliary environments are sufficient for causal graph inference and counterfactual inference.
result Two auxiliary environments are sufficient for identifying causal graphs and counterfactuals.
Bayesian Hierarchical Invariant Prediction refines ICP for better scalability and prior integration.
problem Improving computational scalability and invariance testing for causal inference.
method Bayesian Hierarchical structure to test invariance under heterogeneous data.
result Demonstrated improved scalability and potential as an alternative to ICP.
LaCIM avoids spurious correlation by modeling latent causal factors.
problem Avoiding spurious correlation in supervised learning.
method Introducing latent variables for causal prediction and optimizing over latent space.
result Improved interpretability, robustness, and prediction power on OOD scenarios.
Paper shows hard computational limits for invariant causal prediction.
problem Hard computational limits for invariant causal prediction.
method Distributionally robust estimator with ellipse-shaped uncertain set.
result Estimation error rate can be arbitrarily slow for computationally efficient algorithms.
Paper presents a world model that learns invariant causal features using contrastive unsupervised learning.
problem Learning invariant causal features in unsupervised settings.
method Contrastive unsupervised learning with intervention invariant auxiliary task.
result Significantly outperforms state-of-the-art methods on out-of-distribution point navigation tasks.
Causal invariance can improve finite-sample domain adaptation, but only when the target risk margins are large.
problem Finite-sample domain adaptation
method Linear regression with causal knowledge
result Adaptive aggregation can match best candidate predictor while avoiding negative transfer
The paper tackles policy learning in dynamic environments using causal methods.
problem Existing reinforcement learning algorithms assume static mechanisms, but real-world systems often have changing mechanisms.
method The paper introduces multi-environment contextual bandits and policy invariance to handle environmental shifts.
result An optimal invariant policy is guaranteed to generalize across environments under suitable assumptions.
New method selects causal features from diverse data types.
problem Discovering causal relationships from non-continuous data types.
method Transformation-Model (TRAM) based Invariant Causal Prediction (TRAM-ICP) with TRAM-GCM and TRAM-Wald tests.
result Improved power and type I error control for diverse response types.
Paper analyzes self-supervised learning using causal methods and proposes a new objective.
problem Lack of theoretical understanding of self-supervised learning success.
method Uses a causal framework to enforce invariance constraints on proxy classifiers.
result ReLIC objective improves generalization guarantees and outperforms existing methods.
NOTEARS fails to identify true causal relationships from data.
problem Identifying true causal relationships from observational data.
method NOTEARS aims to identify a parsimonious DAG from data explaining residual variance.
result NOTEARS is not suitable for identifying true causal relationships.
New method identifies stable latent variables across different domains using weak distributional invariances.
problem Learning causal representations for multi-domain datasets.
method Autoencoders incorporating weak distributional invariances.
result Autoencoders can identify stable latent variables across different domains.
A-ICP selects experiments to learn causal effects efficiently.
problem Learning causal effects from observational data is difficult.
method Active learning framework based on Invariant Causal Prediction.
result Proposes intervention selection policies to reveal direct causes.
A method to approximate causal models using information theory.
problem Inferring causal direction and effect between discrete variables.
method Embedding distributions into a higher dimensional space and solving a linear optimization problem.
result Information-theoretic approximation (IACM) can be used for causal discovery in bivariate, discrete cases.
The paper tackles spurious correlations in machine learning models and introduces counterfactual invariance.
problem Spurious correlations in machine learning models that depend on irrelevant parts of input data.
method The paper uses causal inference to stress test models and introduces counterfactual invariance as a formal requirement.
result Counterfactual invariance is a requirement for models to be robust to irrelevant perturbations in input data.
Quandle coloring detects causality in spacetime links.
problem Detecting causality in spacetime links using quandle colorings.
method Conjugation quandle of dihedral group D5 combined with Alexander-Conway polynomial.
result The conjugation quandle over D5 distinguishes causally related and unrelated events.
New algorithm learns invariant representations for robust neural networks.
problem Learning robust neural network representations that are invariant to certain factors.
method Causal perspective and distribution matching approach.
result Empirically, the algorithm achieves state-of-the-art performance on domain generalization.
The paper explores how to make machine learning models robust to domain shifts.
problem Machine learning models are unreliable in domains different from training.
method Introducing a broad formal notion of invariance and causal structures.
result The true underlying causal structure of the data plays a critical role in robustness.
This thesis tackles causality in machine learning, improving OOD generalization and robustness.
problem Machine learning struggles with OOD generalization and robustness due to lack of causality modeling.
method Exploits the principle of independent causal mechanisms (ICM) to ensure conditional distribution invariance under distribution shifts.
result Demonstrates how incorporating causality can enhance machine learning's OOD generalization, interpretability, and robustness.
Causal-NECO VaR improves financial risk assessment under market turbulence.
problem Inaccurate risk assessment in volatile markets.
method Causal Network Contagion Value at Risk (Causal-NECO VaR) using causal network framework.
result Robust and invariant predictive power in unstable financial environments.
New method identifies causal graphs with limited data and noise.
problem Identifying causal graphs from observational data is generally impossible.
method Using additional data from two environments with different noise statistics, and assuming Gaussian noise.
result The entire causal graph can be uniquely identified with a constant number of environments.
We show that order-invariant injective maps on the noncompactly causal symmetric space SO0(1,n)/SO0(1,n−1) belong to O(1,n)+.
New test uncovers causal links in rare event dynamics.
problem Causal discovery for rare event phenomena in dynamic systems.
method Nonparametric conditional independence test on time-invariant data.
result Validated across simulated and real-world datasets.
NICE learns a representation to avoid bad controls in causal inference.
problem Avoiding bad controls in causal inference from observational data.
method Uses invariant risk minimization (IRM) to learn a representation of covariates that avoids bad controls.
result NICE outperforms adjusting for all covariates in cases with unknown collider variables and bad controls.
Reasoning based on causality, instead of association has been considered as a key ingredient towards real machine intelligence. However, it is a challenging task to infer causal relationship/structure among variables. In recent years, an Independent Mechanism (IM) principle was proposed, stating that the mechanism gene…
Paper introduces effect-invariance for better policy generalization.
problem Adapting policies to unseen environments efficiently.
method Introduces effect-invariance, a relaxation of full invariance, and develops testing procedures to test e-invariance directly from data.
result Effect-invariance enables zero-shot and few-shot policy generalization without assuming a causal graph.
The paper analyzes counterfactual invariance and its relation to conditional independence.
problem Understanding the relationship between counterfactual invariance and conditional independence.
method Theoretical analysis of existing definitions, graphical implications, and mathematical proofs.
result Counterfactual invariance implies conditional independence, but not the other way around.
Enhanced symplectic quandle colorings detect causal structure in spacetime diagrams.
problem Detecting causal structure in spacetime diagrams using polynomial invariants.
method Comparing symplectic quandle colorings of different diagrams representing spacetime connections.
result Enhanced symplectic quandle colorings consistently distinguish between causally unrelated and related spacetime configurations.
We discuss contact invariant structures on the space of solutions of a third-order ordinary differential equation. Associated to any third-order differential equation modulo contact transformations, Chern introduced a degenerate conformal Lorentzian metric on the space of 2-jets of functions of one variable. When the W…
Discovering causal relationships is a hard task, often hindered by the need for intervention, and often requiring large amounts of data to resolve statistical uncertainty. However, humans quickly arrive at useful causal relationships. One possible reason is that humans extrapolate from past experience to new, unseen si…
New framework detects directional influence in multivariate time series.
problem Detecting directional influence in multivariate time series.
method Order-constrained spectral non-invariance.
result Unique diagnostic functional for directional influence.
The paper explores how to apply causal knowledge across different datasets to improve learning.
problem How to apply causal knowledge across different datasets to improve learning.
method Investigates the structural causal bandit with transportability, fusing priors from source environments to enhance learning in the deployment setting.
result Achieves a sub-linear regret bound with an explicit dependence on informativeness of prior data, potentially outperforming standard bandit approaches.
The Links--Gould polynomial detects causality in spacetimes.
problem Detecting causality in spacetimes using link invariants.
method Using the Links--Gould polynomial, which specializes to the Alexander--Conway polynomial.
result The Links--Gould polynomial distinguishes Allen-Swenberg links and detects causality.
Study nondifferentiable metrics in general relativity, resolving causality issues and limits evolution scenarios.
problem Causality issues and evolution scenarios in black hole interiors with closed timelike geodesics.
method Method of equivalence on Courant algebroids to derive new differential invariants.
result Resolved causality issues and limited evolution scenarios for gravitational collapse.
SCTL scales causal domain adaptation without prior knowledge.
problem Domain adaptation with covariate shift and invariances across domains.
method SCTL: scalable causal discovery algorithm based on Markov blanket.
result SCTL achieves scalable and robust domain adaptation.
AI needs causal inference to avoid being just a correlation machine.
problem AI's inability to distinguish correlation from causation.
method Develops a unified framework connecting various causal statistical estimators and proves a Statistical Necessity Theorem for causal generalization.
result AI systems without causal grounding are brittle and biased, highlighting the need for causal statistics.
The concept of causality has a controversial history. The question of whether it is possible to represent and address causal problems with probability theory, or if fundamentally new mathematics such as the do-calculus is required has been hotly debated, In this paper we demonstrate that, while it is critical to explic…
ICIL learns policies invariant to multiple environments, improving generalization.
problem Learning policies from multiple environments leads to spurious correlations.
method ICIL learns invariant feature representations and a matching imitation policy.
result ICIL policies generalize better to unseen environments.
Novel framework uses causality for financial forecasting.
problem Balancing invariance and prediction accuracy in financial time series.
method Causality-inspired models for forecasting asset returns.
result Efficacy in stable and accurate predictions, especially in turbulent markets.
Framework tackles OOD challenges in molecule property prediction by modeling environments.
problem Challenges in modeling OOD samples for molecule property prediction.
method Soft causal learning framework incorporating chemistry theories and cross-attention mechanisms.
result Demonstrates well generalization ability on seven datasets.
Game theory approach to predicting and responding to interventions based on causal relationships.
problem Optimizing predictions and interventions in response to observational data.
method Prediction-intervention game framework, focusing on invariant subsets of covariates.
result Stable-blanket predictors are optimal for certain follower objectives and under specific conditions.